A powerful Python-based network analysis tool for building, measuring, and visualizing complex graph structures using NetworkX.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install graph-analysis
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install graph-analysis using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Graph Analysis?
The Graph Analysis skill leverages the industry-standard NetworkX library to transform raw relational data into actionable insights. By integrating directly into your AI agent workflow, this Openclaw Skills extension allows for deep exploration of topological properties, connectivity, and structural patterns within any dataset.
Whether you are dealing with social networks, infrastructure maps, or hierarchical organizational data, this skill provides the mathematical rigor needed for advanced graph theory applications. It automates the process of node and edge creation, algorithmic computation, and high-quality visual rendering, making complex network science accessible through simple AI commands.
Graph Analysis Use Cases
- Identifying influential stakeholders or bottlenecks in a supply chain using centrality metrics.
- Mapping and visualizing social media interactions to detect organic community clusters.
- Optimizing logistics and routing by calculating weighted shortest paths between nodes.
- Analyzing systemic risk in financial networks by measuring graph density and connectivity.
- Generating synthetic network models like Scale-free or Small-world graphs for simulation and testing.
How Graph Analysis Works
- Data Ingestion: The skill parses structured data from CSV, JSON, or inline lists to define nodes and edges.
- Graph Construction: It initializes the appropriate graph type, such as directed (DiGraph) or undirected (Graph) structures.
- Algorithmic Processing: The agent executes specialized algorithms including PageRank, Louvain community detection, or betweenness centrality.
- Visual Synthesis: Using Matplotlib with the Agg backend, the skill generates PNG visualizations with custom layouts like Spring or Kamada-Kawai.
- Insight Delivery: The results, statistics, and generated images are returned to the user for final review.
Graph Analysis Setup
To enable graph processing capabilities, ensure python3 and pip are installed, then run the following command to install the required Openclaw Skills dependencies:
pip install networkx matplotlib numpy --break-system-packages
Graph Analysis Data Schema & Taxonomy
The Graph Analysis skill organizes data into structured graph objects and exports visual assets as follows:
| Attribute | Description |
|---|---|
| Nodes | Entities within the network, often containing metadata attributes. |
| Edges | Relationships between nodes, which can be weighted or directed. |
| Centrality | Metrics including Degree, Betweenness, and Closeness stored as dictionaries. |
| Communities | Subsets of nodes identified through modularity or Louvain clustering. |
| Output Files | Visualizations are typically saved as graph.png or communities.png. |
Graph Analysis Advanced Features
- Multi-layout visualization support including spring, circular, and shell layouts for optimal spatial representation.
- Scalable community detection using the Louvain method, optimized for large-scale graphs exceeding 10,000 nodes.
- Custom node styling where size and color can be programmatically mapped to centrality scores or community membership.
- Support for weighted pathfinding, allowing for more realistic distance and cost calculations in complex networks.
SKILL.md
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